A Probabilistic Approach for Color Correction in Image
Mosaicking Applications
ABSTRACT
A
probabilistic
color
correction
algorithm
for
correcting
the
photometrical disparities. First, the image to be color corrected is
segmented into several regions using mean shift.
Then, connected regions are extracted using a region fusion algorithm.
Local joint image histograms of each region are modeled as collections of
truncated Gaussians using a maximum likelihood estimation procedure.
Then, local color palette mapping functions are computed using these sets
of Gaussians. The color correction is performed by applying those
functions to all the regions of the image. An extensive comparison with
ten other state of the art color correction algorithms is presented, using
two different image pair data sets.
Results show that the proposed approach obtains the best average scores
in both data sets and evaluation metrics and is also the most robust to
failures.
ARCHITECTURE
EXISTING SYSTEM
Image
mosaicking
applications
require
both
geometrical
and
photometrical registrations between the images that compose the mosaic.
Several previous works on color correction have shown the advantages of
using a local approach when compared to a global one. Local methods
presented in and have shown better performance compared with the global
approach presented in .
PROPOSED SYSTEM
proposed one of the first parametric methods for color correction.
Single Gaussians are used to model the color distributions of the
target and source images.
The color distribution of the S image is then transferred to the T
image by scaling and offsetting according to the mean and standard
deviations of the precomputed Gaussians.
With this in mind, some subsequent works have proposed to use
more complex models, in an attempt to achieve more accurate
models of the color distributions, and as a result, more effective
color corrections.
The usage of Gaussian Mixture Models (GMMs) was proposed in .
The GMM was fitted to the data using a Expectation Maximization
(EM) methodology. The GMM could not only model the color
distribution more accurately, but also assist in the probabilistic
segmentation of the image into regions. For each segmented
region, a color transfer methodology similar to the one proposed in
was proposed. One other problem that may hamper the
performance of color correction methodologies is that each color
channel in the image is modelled and corrected independently.
Local joint image histograms of each region are modeled as
collections of truncated Gaussians using a maximum likelihood
estimation procedure.
Then, local color palette mapping functions are computed using
these sets of Gaussians.
The color correction is performed by applying those functions to
all the regions of the image.
An extensive comparison with ten other state of the art color
correction algorithms is presented, using two different image pair
data sets.
Results show that the proposed approach obtains the best average
scores in both data sets and evaluation metrics and is also the most
robust to failures.
MODULES
Modules
1. User Registration
2. Upload Image
3. Color correction
4. Image mosaicking
5. Local joint image histograms
6. JPEG QUANTIZATION NOISE ANALYSIS
A. Notations
B. Quantization Noise
C. General Quantization Noise Distribution
6. Identification of decompressed jpeg images based on quantization
noise
Analysis
A. Forward Quantization Noise
B. Noise Variance for Uncompressed Images
C. Noise Variance for Images With Prior JPEG Compression
Modules Description
1. Color correction:
Color correction algorithm for correcting the photometrical disparities
The color correction is performed by applying those functions to all the
regions of the image.
An extensive comparison with ten other state of the art color correction
algorithms is presented, using two different image pair data sets.
The material includes images with color correction results from all tested
approaches
This process of photometrical alignment or calibration is referred to as
color correction between images Although there are several methods
proposed to deal with color correction, most involve strong assumptions,
which are in general, difficult to fulfill in complex environments.
Furthermore, the size of the data sets used for evaluating their
performance is relatively small.
Image mosaicking :
To the best of our knowledge, this paper also presents one of the most
complete evaluations of color correction algorithms for image
mosaicking published in the literature.
An extensive comparison, which includes nine other approaches, two
datasets with over sixty image pairs and two distinct evaluation
metrics, is presented
IMAGE mosaicking and other similar variations such as image
compositing and stitching have found a vast field of applications
ranging from satellite or aerial imagery to medical imaging street
view maps city
super-resolution
reconstruction to name a few.
texture synthesis
or stereo
In general, whenever merging two or more images of the same scene
is required for comparison or integration purposes, a mosaic is built.
Two problems are involved in the computation of an image mosaic:
the geometric and the photometric correspondence
Local joint image histograms:
Local joint image histograms of each region are modeled as collections of
truncated Gaussians using a maximum likelihood estimation procedure.
In higher-dimensional Bezier patches were used to represent color
transfer functions.
Nonparametric methods also include approaches that use the image
histograms to obtain a color transfer function.
In and the entire probability density function is mapped without making
assumptions on its nature.
There is also a group of techniques called nonparametric kernel
regressions, which could have a direct application to the color correction
problem, in particular when formulated using the joint image histogram.
The joint image histogram. Since the image is split up into several
regions one joint image histogram is computed for each color segmented
region.
Thus, to highlight the fact that they correspond to a region in the image,
we refer to these histograms as local joint image histograms.
Other authors have employed joint image histograms for color correction
applications but their histograms are not local.
Identification Of Decompressed Jpeg Images Based On Quantization Noise
Analysis
From above, we know that the quantization noise distributions are
different in two JPEG compression cycles. In the following, we first
define a quantity, call forward quantization noise, and show its relation to
quantization noise. Then, we give the upper bound of its variance, which
depends on whether the image has been compressed before. Finally, we
develop a simple algorithm to differentiate decompressed JPEG images
from uncompressed images.
Evaluation on JPEG Images From a Database With Random Quality
Factors
Since the decompressed JPEG images encountered in daily life are
coming from different sources, and thus having been compressed with
varying quality factors. We conduct the following experiment to show the
performance on random quality factors.
Test Image Set: To increase the amount and the diversity of images for
testing, and also to test whether the thresholds of the methods heavily rely
on image database, we use the test image set composed of 9,600 color
JPEG images created by Fontani et al.
A. Internet Image Classification
The first application of our JPEG identification method is Internet
image classification. Internet search engines cur gently allows users to
search by content type, but not by compression history. There may be
some graphic designers who wish to differentiate good-quality
decompressed images from uncompressed images in a set of images
returned by Internet search engines. In this case, searching images by
compression history is important. In this section, we show the feasibility
of such an application.
Image Classification Algorithm: We first convert color images into
gray-scale images. Then we divide each image into non-overlapping
macro-blocks of size B × B (e.g., B = 128, 64, or 32). If the dimension of
the image is not exactly the multiple times of B, the last a few rows or
columns are removed from testing. Next, we perform JPEG identification
on each macro-block. We can use the threshold as given in Table I for
each macro-block size. For a test image I, suppose it contains a total
number of N(B) macro-blocks, and assume a number of D(B) macroblocks are identified as decompressed. We use a measuring quantity,
called block hit (BT), to assess the proportion of macro-blocks being
identified.
SYSTEM REQUIREMENT SPECIFICATION
HARDWARE REQUIREMENTS
 System
:
Pentium IV 2.4 GHz.
 Hard Disk
:
80 GB.
 Monitor
:
15 VGA Color.
 Mouse
:
Logitech.
 Ram
:
512 MB.
SOFTWARE REQUIREMENTS
 Operating system
:
Windows 7 Ultimate
 Front End
:
Visual Studio 2010
 Coding Language
:
C#.NET
 Database
:
SQL Server 2008